The fingerprint identification technology has been widely used in various fields, including crime investigation, physical and logical access control, time and attendance. During sampling the fingerprint, fingerprints with a non-standard sampling pose (i.e., the pressing position of the fingerprint is not in the center of the image or the angle of the fingerprint is not vertical) are easy to be generated due to lack of a common pressing standard. In order to identify these fingerprints with non-standard sampling pose, various possible spatial transformations have to be considered in the fingerprint matching algorithm, thus increasing the calculation complexity.
At present, the conventional fingerprint pose estimation methods are based on the feature points of the fingerprint (such as the singular point, the point with a highest curvature on the ridge line). However, these feature points require the fingerprint image should have a high quality and the detection of the feature points is not stable. Especially for the arch pattern, it is more difficult to detect the feature points stably. As a preprocessing step, the error of the fingerprint pose estimation usually results in the failure of the succeeding fingerprint matching algorithm directly. With the fingerprint pose estimation methods having a small error, the spatial transformations in the fingerprint matching algorithm may be effectively reduced, and the efficiency and accuracy of the fingerprint identification may be greatly improved.